The physiological consequences of catch‐and‐release angling: perspectives on experimental design, interpretation, extrapolation and relevance to stakeholders
Bibliographic record
Abstract
Abstract Over the past 20 years, there has been a dramatic increase in the use of physiological tools and experimental approaches for the study of the biological consequences of catch‐and‐release angling practices for fishes. Beyond simply documenting problems, physiological data are also being used to test and refine different strategies for handling fish such that stress is minimised and survival probability maximised, and in some cases, even for assessing and facilitating recovery post‐release. The inherent sensitivity of physiological processes means that nearly every study conducted has found some level of – unavoidable – physiological disturbance arising from recreational capture and subsequent release. An underlying tenet of catch‐and‐release studies that incorporate physiological tools is that a link exists between physiological status and fitness. In reality, finding such relationships has been elusive, with further extensions of individual‐level impacts to fish populations even more dubious. A focus of this article is to describe some of the challenges related to experimental design and interpretation that arise when using physiological tools for the study of the biological consequences of catch‐and‐release angling. Means of overcoming these challenges and the extrapolation of physiological data from individuals to the population level are discussed. The argument is presented that even if it is difficult to demonstrate strong links to mortality or other fitness measures, let alone population‐level impacts of catch‐and‐release, there remains merit in using physiological tools as objective indicators of fish welfare, which is an increasing concern in recreational fisheries. The overarching objective of this paper is to provide a balanced critique of the use of physiological approaches in catch‐and‐release science and of their role in providing meaningful information for anglers and managers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.428 | 0.387 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".